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ANOVA effects can be included in multiple linear regression models in multiple ways, most simply with dummy variables, but then the model has containment issues. Contrast coding is also widely used, mainly to provide a full-column-rank copy of the same model. Then a natural sum of squares to test an effect is the extra SSE due to omitting its terms from the model. In general, in unbalanced models, it is cumbersome to establish exactly what such extra SSEs test – that is, what their non-centrality parameters are as functions of the cell means. Determining and testing only estimable parts may require extra analysis and computing. And, after all that, there is the question of whether another numerator sum of squares might be better. It is established here that, with contrast coding, the model formed by deleting an ANOVA effect's terms is the correct restricted model for that effect; that the non-centrality parameter of the resulting extra SSE is zero if and only if all estimable linear functions of the effect are zero; and that no other numerator sum of squares that tests the effect has a greater non-centrality parameter or lesser numerator degrees of freedom. [ABSTRACT FROM AUTHOR]

This study integrates various remote sensing datasets to analyze environmental changes and their impacts on ecosystems across Pakhtunkhwa Province in Pakistan. Precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) dataset, along with vegetation health assessments using Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data from the Landsat dataset, were used to comprehensively analyze the impact of vegetation dynamics on environmental footprints (i.e., temperature, precipitation, and LST). Land use maps, generated through supervised classification of Landsat images from 1985 to 2023, highlight significant changes in different land use classes, including vegetation and forest cover. Bayesian Network Modelling (BNM) and Dummy Variable Regression (DVR) methods were employed to assess the impact of vegetation (using NDVI time series) on environmental footprint and forest cover in particular. The results suggest that the NDVI generally increase the cooling effect across most of the study area, indicating that higher vegetation density is linked to a decrease in temperatures. This inverse relationship is also apparent in the connection between the NDVI and the LST, depicting a negative trend in surface temperature over most of the pixels/districts. The regression coefficients for the NDVI and the LST vary across different pixels, ranging from −5.3839 °C to 5.2697 °C, with standard deviations from 2.057 °C to 5.138 °C, reflecting a variability in the strength of this cooling effect. Similarly, for the relationship between the NDVI and the LST, coefficients range from −7.1513 °C to 6.6322 °C, with standard deviations between 1.612 °C and 4.155 °C. In contrast, NDVI and precipitation show a positive relationship, with regression coefficients ranging from 4.1686 °C to 44.3932 °C and standard deviations between 2.242 °C and 8.224 °C, suggesting greater variability in precipitation corresponding to vegetation dynamics. Additionally, forest cover generally correlates positively with precipitation in most pixels, but the variability across pixels emphasizes the complex nature of these relationships. The study identified substantial fluctuations in land use categories over the decades, indicating environmental shifts driven by both natural and human factors. BNM demonstrated a positive impact of vegetation dynamics on precipitation and a negative impact on both temperature and LST. On the other hand, the increase in forest cover, particularly due to the Billion Tree Tsunami Project, has a significant impact on the environmental footprint identified through DVR. By combining high-resolution datasets with advanced statistical techniques, this study offers key insights into the dynamic interactions between land cover, vegetation, and climate in the study region, providing valuable information for sustainable environmental management. [ABSTRACT FROM AUTHOR]

The aim of this work is to explore the relationship between the skills acquired by students of Engineering in the Nigerian university education system and the demographic characteristics of the students, such as gender and age, which could influence the attainment of these skills. A total of 112 students were surveyed to determine which of 24 stated skills they possess. Afterwards, dummy variable multiple regression technique was used for analysis. Results showed a significant relationship between gender especially, and age of students to a lesser degree, and the total number of skills acquired by the students. Specifically, the study was able to show that being a male student is more likely to lead to a student acquiring an average of about 1.79 more skills than his female counterparts. This is so if the values of all other variables such as student age and source of the skills acquisition are held at constant levels. The result presented in this work was also able to show that students who engage external sources and activities for skills acquisition fare better than students who do not. [ABSTRACT FROM AUTHOR]

Over the last three decades, many growth and yield systems developed for the southeast USA have incorporated methods to create a compatible basal area (BA) prediction and projection equation. This technique allows practitioners to calibrate BA models using both measurements at a given arbitrary age, as well as the increment in BA when time series panel data are available. As a result, model parameters for either prediction or projection alternatives are compatible. One caveat of this methodology is that pairs of observations used to project forward have the same weight as observations from a single measurement age, regardless of the projection time interval. To address this problem, we introduce a variance–covariance structure giving different weights to predictions with variable intervals. To test this approach, prediction and projection equations were fitted simultaneously using an ad hoc matrix structure. We tested three different error structures in fitting models with (i) homoscedastic errors described by a single parameter (Method 1); (ii) heteroscedastic errors described with a weighting factor |${w}_t$| (Method 2); and (iii) errors including both prediction (⁠|$\overset{\smile }{\varepsilon }$|⁠) and projection errors (⁠|$\tilde{\varepsilon}$|⁠) in the weighting factor |${w}_t$| (Method 3). A rotation-age dataset covering nine sites, each including four blocks with four silvicultural treatments per block, was used for model calibration and validation, including explicit terms for each treatment. Fitting using an error structure which incorporated the combined error term (⁠|$\overset{\smile }{\varepsilon }$| and |$\tilde{\varepsilon}$|⁠) into the weighting factor |${w}_t$| (Method 3), generated better results according to the root mean square error with respect to the other two methods evaluated. Also, the system of equations that incorporated silvicultural treatments as dummy variables generated lower root mean square error (RMSE) and Akaike's index values (AIC) in all methods. Our results show a substantial improvement over the current prediction-projection approach, resulting in consistent estimators for BA. [ABSTRACT FROM AUTHOR]

Background: Individuals working excessive hours is a worldwide phenomenon. In Indonesia, over 32 million people work more than 40 h per week, contributing to around 26% of the workforce. Excessive working may affect health, increasing the risk of cardiovascular diseases such as hypertension. Hypertension affected around 34% of Indonesian adults, approximately 63.3 million people and led to about 427,000 deaths in 2018, and the prevalence remains high at 29.2% in 2023. This study aims to analyze the relationship between work hours and the risk of hypertension among working individuals in Indonesia. Methods: This study used a pooled cross-sectional data from the Indonesian Family Life Survey (IFLS) wave 4 (2007) and wave 5 (2014) and performed a logit regression analysis to examine the likelihood of a working individual having hypertension based on the individual's work hours. A dummy variable of hypertension is created based on the result of blood pressure measurement. The sample consists of 22,500 working individuals in Indonesia. This study controlled for job characteristics, sociodemographic status and health-behavioral risk factors such as BMI and smoking behavior, and performed additional regression analyses for alternative models to check for robustness. Results: Our findings showed that there is a higher probability of having hypertension for workers who work longer hours by 0.06% points for each additional hour of work (p < 0.01). Other factors such as physical activity and smoking behavior have also been demonstrated to be significantly correlated to the risk of hypertension. Conclusions: This study revealed a positive relationship between work hours and hypertension. Although this study cannot suggest causality, the strongly significant correlation may provide an idea and an overview regarding the risk of hypertension among working individuals in Indonesia. The Indonesian government could consider conducting further studies to implement and promote flexible working arrangements initiatives and incentive programs to improve workers' health outcomes. [ABSTRACT FROM AUTHOR]

This study focused on the natural birch forest (Betula platyphylla) in the Daxing'an Mountains of Yakeshi Forestry Administration in Inner Mongolia. Using 198 sample plots, the relationship between carbon density in the natural birch forest stand and various stand variables was analyzed. A basic model for predicting carbon density was established, incorporating site conditions (grass birch forest, rhododendron-vaccinium birch forest, hazelnut birch forest) as dummy variables to predict stand carbon density across different forest types. This paper offered insights and methodologies for constructing carbon density models and advancing forest carbon sequestration in forestry research. The results showed that the determination coefficient (R²) for the basic model of carbon density in the natural birch forest was 0. 703, root mean square error (RMSE) was 8. 615 t/hm-2, Akaike information criterion (AIC) was 841. 206, and Bayesian Information Criterion (BIC) was 851. 071. After site conditions were introduced as dummy variables, R2 increased to a maximum of 0. 818, and RMSE were all less than or equal to 8. 241 t/hm-2, indicating that the model had good stability and the predicted parameters were more accurate. AIC for the dummy variable model was less than or equal to 541. 431, and BIC was less than or equal to 550. 320. The dummy variable model can reflect the change of carbon density under different site conditions, and both the fitting and testing of the model show that it is suitable for the prediction of forest carbon density in the study area, which pro⁃ vides a reference for the estimation of natural birch forest carbon density [ABSTRACT FROM AUTHOR]

The boundary at anode was generally assumed to be impermeable in previous work. However, the horizontal electro-kinetic geosynthetics are typically arranged at intervals, forming a permeable anode that allows pore water in original soil to flow through the gaps, which inevitably affects the consolidation process of electroosmotic layer. A one-dimensional analytical model for the coupled consolidation of original soil and electroosmotic layer is established to study this effect. The rationality of the one-dimensional model is justified by comparing the results of a two-dimensional model. To obtain the analytical solution, new dummy variables are proposed to homogenize the continuity conditions. The analytical solution is derived using the separate variable method and verified by existing analytical solutions and numerical results. Parametric analysis reveals that the consolidation time would be more significantly delayed in the thicker original soil. Both the permeability and compressibility of original soil influence the delayed effect on the consolidation of electroosmotic layer but the latter is more significant. For the equivalent thicknesses of original and electroosmotic layers, the time to reach 90% average degree of consolidation increases up to more than 6 times for the compression coefficient ratio of original and electroosmotic layers being equal to two. [ABSTRACT FROM AUTHOR]

Purpose: This study empirically examines the moderating role of geopolitical risk on the tourism–economic growth nexus by applying a recent geopolitical risk indicator developed by Caldara and Iacoviello (2022) in a cross-country panel data growth model context for a sample of 24 countries. Design/methodology/approach: A Dummy Variable Least Squares panel data model, nonparametric covariance matrix estimator and SYS-GMM estimation techniques are employed for the analysis. The authors capture the GPR moderating effect by disaggregating the cross-country sample according to low versus high country GPR score and through a GPR interaction coefficient. Several controls are included in the models such as gross fixed capital formation and—consistent with Barro (1990)—government consumption. Trade openness is used to account for the export-led growth effect. In line with neoclassical growth theory (e.g. Barro, 1991), the authors also include the real interest rate, to account for policy makers' commitment to macroeconomic stability, financial depth, as a proxy for financial development, population growth and the level of secondary school education. The authors also control for unobserved country-specific and time-invariant effects. Findings: The research finds that the interaction term of geopolitical risk significantly contributes to the predictive ability of the regression and provides empirical evidence that confirms that only in low geopolitical risk countries international tourism positively and significantly contributes to economic growth. Important theoretical and policy implications flow from these findings. Originality/value: The study not only contributes to advancing academic knowledge on the tourism–growth nexus, it also has impact beyond academia. Many countries have in the past pursued and many continue to pursue, tourism specialization and/or tourism-led growth strategies based on the theoretically well-established and empirically validated positive link between inbound tourism and economic growth. The findings alert policy makers in such countries to the significant moderating role that geopolitical risk plays in affecting the above-mentioned relationship and to the importance of prioritizing geopolitical stability as a policy precursor for the successful implementation of such strategies. [ABSTRACT FROM AUTHOR]

The banking sector is vital to economic activity when it develops. Its development depends on the ability of banks to generate sufficient profits to meet their commitments. In this context, we set ourselves the objective of analyzing the effect of bank profitability on economic growth. The study covers the period from 1996 to 2023, and covers 7 WAEMU countries except Guinea-Bissau, for lack of data. Methodologically, we apply Bruno's (2005) Least Square Dummy Variable (LSDVC) bias correction approach to the dynamic unbalanced panel data model. The results indicate that bank profitability has a positive influence on economic growth in the WAEMU region, whatever the profitability index used. Inflation,Political stability and investment are not unrelated to economic growth in the union. It follows from these results that the union's authorities need to strengthen supervision of the banking sector to ensure that it remains profitable. [ABSTRACT FROM AUTHOR]

Copyright of EURE is the property of Pontificia Universidad Catolica de Chile and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Latinoamericana de Estudios Urbano Regionales is the property of Pontificia Universidad Catolica de Chile and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ekonomik Yaklaşim is the property of Ekonomik Yaklasim Dernegi (Ekonomik Yaklasim Association) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: Under the background of low consumer financial knowledge and accumulated credit card liabilities, this study investigates the relationship between financial knowledge and responsible credit card behavior using data from the 2019 China Household Finance Survey (CHFS). From the perspective of consumer economic well-being, this study defines accruing credit card debt to buy houses and cars when loans with lower interest rates are available as irresponsible credit card behavior. Design/methodology/approach: This study uses probit regressions to examine the association between financial knowledge and responsible credit card behavior because the dependent variable is a dummy variable. To alleviate endogeneity problems, this study uses instrument variables and Heckman's two-step estimation. Furthermore, to explore the potential mediators in this process, this study follows the stepwise regression method. Finally, this study introduces interaction terms to examine whether this association differs in different groups. Findings: The results indicate that financial knowledge is conducive to increasing the probability of responsible credit card behavior. Mediating analyses reveal that the roles of financial knowledge occur by increasing the degree of concern for financial and economic information and the propensity to plan. Moderating analyses show that the effects of financial knowledge on responsible credit card behavior are stronger among risk-averse consumers and in regions with favorable digital access. Originality/value: This study measures responsible credit card behavior from the perspective of the consumer's well-being, which enriches practical implications for consumer finance. Furthermore, this study explores the potential mediators influencing the process of financial knowledge that affects responsible credit card behavior and identifies moderators to conduct heterogeneous analyses, which helps comprehensively understand the nexus between financial knowledge and credit card behavior. By achieving these contributions, this study helps to curb the adverse effects of irresponsible credit card behavior on consumers' well-being and the economic system and helps policymakers promote financial knowledge to fully prevent irresponsible credit card behavior. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effect of inter-country wage differentials on textile and garment (T&G) exports of Bangladesh and present an account of the important issue of why textile and garment exports are directed from developing countries to developed countries. Our empirical results imply that the wage differentials between developed and developing countries constitute one of the main drivers of T&G exports. In addition, we checked other factors such as the Multi-Fiber Arrangement (MFA) and the Generalized System of Preferences (GSP) by including dummy variables representing the MFA quota withdrawal and the GSP. We found that removal of the MFA had a significant negative effect, as expected. Our study confirms the positive correlation between GSP and exports. After phasing out the MFA and its quota system, the wage differentials not only boost the T&G exports from Bangladesh, but also increase the effectiveness of GSP. Given such findings, we discuss relevant policy implications. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research employs the ARDL (Autoregressive Distributed Lag) model to examine the impact of COVID-19 on mutual trade between Germany and the Visegrad Four (V4). We focus on trade with Germany due to its status as the strongest and geographically closest trade partner of these four post-communist countries. Additionally, the extensive value chains of industrial production create robust economic connections between Germany, as an industrial leader, and the adjacent states, which is evident in the frequent mutual trade. The pandemic caused a sudden disruption of these value chains, particularly in the manufacturing sector, which dominates the region. In our analysis, we estimated the ARDL model for each country, considering both export and import data. Initially, we estimated long-run models, revealing that the coefficients of import and export models for the countries yielded different long-term results. In the short-run models, the dummy variables for both export and import models across all countries were significant, indicating that COVID-19 affected international trade between the V4 and Germany. Moreover, in all models, the coefficient of the dummy variable was negative, suggesting that COVID-19 had a detrimental effect on international trade. An important finding is that, depending on the nature of the dummy variable used, the effect of COVID-19 was observed during the pandemic period, with international trade returning to its previous trend post-pandemic. This experience highlights that while predicting the next pandemic and its negative impacts remains challenging, countries and their production chains are now better prepared. They have adapted to operate with greater flexibility, enabling them to effectively face similar unexpected challenges in the future. [ABSTRACT FROM AUTHOR]

In this research, within the specified years, corn in Türkiye by years; It is aimed to determine the existence of a causality between the production amount, cultivation area, yield, export amount, import amount and economic crises added as dummy variables. For this purpose, corn production, corn cultivation area, corn yield, corn export amount and corn import amount in the statistical tables between 1995 and 2020 published by the Turkish Grain Board (TMO; 2021). In addition, the crisis years were added to the data set as a dummy variable to see the crisis effect. In the research, Augmented Dickey Fuller method is the test that checks whether the series is stationary. After deciding that the series is non-stationary with the ADF test, the most appropriate lag length was determined with the VAR (Variance Autoregressive Model) model, and the analysis was carried out with the help of the VAR Granger Causality method. According to the estimation results, corn in the statistical tables between 1995-2020; In the data set, in which the crisis years were added as a dummy variable to see the effects of the crisis in terms of production, cultivation area, yield, export amount and import amount; First difference is stationary, Akaike Information Criterion is the smallest, 2nd lag length is the most suitable lag length, and corn; corn; It has been determined by the analysis that production amount, import amount and economic crises are a one-way cause of exports, and corn cultivation area is a one-way cause of economic crises. [ABSTRACT FROM AUTHOR]

In this article, a robust estimation method is proposed for a partially linear panel data model with fixed effects. We eliminate the fixed effects based on auxiliary linear regression, then approximate the unknown non parametric component with B-spline function, and obtain the robust estimators of the parametric and non parametric components by combining projection matrix with exponential squared loss function. Under some regularity conditions, the asymptotic properties of the resulting estimators are proved. Some simulation studies illustrate that the proposed method is more robust than the semiparametric least squares dummy variable estimator. The proposed procedure is illustrated by a real data application. [ABSTRACT FROM AUTHOR]

This paper constructs a unique dataset of clean energy and collects firm-level green innovation data that matches it with provinces and cities. Then, this paper executes quasi-natural experiments on green innovation to examine the effects of China's emission trading policy on green innovation. Further, this paper introduces the interactive terms of the pilot time and pilot area dummy variables into the spatial Durbin model (SDM) and spatial dynamic panel model (SDPM) to construct quasi-natural experiments to examine the spatial effect. The findings confirm a negative association between the 'policy treatment effect' and green innovation. The results also indicate a significant negative association between the emissions trading policy and clean energy. Besides, the empirical results reveal a positive correlation between clean energy and green innovation; clean energy promotes green innovation in the region and the surrounding areas. This paper complements existing research and provides empirical evidence and policy implications for developing clean energy, promoting green innovation, and improving the emission trading policy. [ABSTRACT FROM AUTHOR]

This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Our findings reveal significant shifts in volatility dynamics and day-of-the-week effects on returns, challenging the notion of market efficiency. Notably, Bitcoin and Solana began exhibiting day-of-the-week effects during the pandemic, whereas Cardano and Dogecoin did not. During the pandemic, Binance USD, Ethereum, Tether, USD Coin, and Ripple showed multiple days with significant day-of-the-week effects. Notably, positive returns were generally observed on Sundays, whereas a shift to negative returns on Mondays was evident during the COVID-19 period. These patterns suggest that exploitable anomalies persist despite the market's continuous operation and increasing maturity. The presence of a long-term memory in volatility highlights the need for robust trading strategies. Our research provides valuable insights for investors, traders, regulators, and policymakers, aiding in the development of effective trading strategies, risk management practices, and regulatory policies in the evolving cryptocurrency market. [ABSTRACT FROM AUTHOR]

Objective: We propose a simple approach to dealing with partial contestation in models of multiparty elections. Methods: Our proposed approach is to add a tiny value to the vote share of parties that do not contest a district and then to include dummy variables identifying those districts in which parties do not compete. We can then estimate a single system of equations using a seemingly unrelated regression (SUR) approach and Aitchison's log‐ratio transformation. In our SUR system, we interact the dummy variable for a party that partially contested districts with other predictors in the equation that uses the share of votes of the same party in the log‐ratio outcome. Finally, we estimate robust standard errors for predictors in this equation to address heteroscedasticity. Results: We demonstrate the utility of our approach using simulated data and election results from the English parliamentary elections in 2017. Conclusion: From our simulations, we find that our recommended approach performs as well as that proposed by Tom, Tucker, and Wittenberg. Our strategy is advantageous in that it is easy to estimate, uses information from all districts, and addresses partial contestation in real‐world elections with a single system of seemingly unrelated regressions. [ABSTRACT FROM AUTHOR]

This study examines the impact of government spending across various sectors on poverty in Indonesia, motivated by the need to understand how fiscal policies affect the well-being of people experiencing poverty. Using Ordinary Least Square (OLS) and Least Square Dummy Variable (LSDV) models, the findings reveal that government spending on public services, health, education, and social protection significantly affects the number of poor people. However, spending on public services and education shows a positive coefficient, which may result from mandatory spending regulations not directly aimed at improving welfare. In contrast, government spending in the economic sector has an insignificant impact on poverty, indicating that the effects may require more than one period to manifest. Further analysis is necessary to explore this relationship. The consistency of the results was enhanced by incorporating district/city status as a predictor. These findings highlight the need for a more targeted approach to government spending to reduce poverty in Indonesia effectively. [ABSTRACT FROM AUTHOR]

This study examines the empirical validity of the Linder hypothesis for India's service sector exports from 2005 to 2021, focusing on 35 major importing countries. We use a gravity model trade, applying Feasible Generalised Least Squares (FGLS) and two-step system generalised method of moments (GMM), incorporating country- and time-fixed effects. Our results confirm that the Linder hypothesis does not hold for Indian service exports, revealing an increase in trade intensity between countries with dissimilar income levels. The study finds that distance has a positive and significant impact on Indian service exports. Exchange rates have a negative and significant impact on India's service exports, while the results for the RTA dummy variable are inconclusive. Sharing a common border, a common colony, and a language has a positive and significant effect on Indian service exports. [ABSTRACT FROM AUTHOR]

Copyright of Mexican Journal of Economics & Finance / Revista Mexicana de Economia y Finanzas is the property of Instituto Mexicano de Ejecutivos de Finanzas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Restricted by economic and social development conditions and the level of domestic regulatory system construction, the traditional host country-centered regulatory approach makes it difficult to ensure "responsible investment" by overseas farmland investment companies. The jury is still out on whether international law can directly impose obligations and responsibilities on companies. Therefore, it is realistically necessary for the investor's home country to regulate any behavior that infringes on the human rights and environment of residents of the host country during the company's overseas farmland investment. We used a panel fixed effects model and the dummy variable method to construct regression indicators based on international treaties. This study finds that promulgating responsible investment treaties can significantly promote the scale and number of international farmland investments, which also further led to the problem of differentiation among overseas farmland investors. We further discussed the lack of binding force of current international rules and the difficulty in coordinating the interests of multiple parties. In the regulatory system of the home country, there are still problems, such as insufficient supply of adequate laws, and lack of administrative guidance. Measures such as deepening negotiations on international rules for responsible overseas farmland investment, further leveraging the role of land ownership in responsible overseas farmland investment, improving existing domestic legislation, and strengthening administrative guidance on overseas farmland investment can be used as countermeasures. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the moderating effect of sustainability reporting on the relationship between the independent variables of board diversity, and earnings management and the dependent variable of readability of financial statements. Design/methodology/approach: The study panel data regression analysis involved 36 Kenyan-listed companies from 2016 to 2020. Findings: Key findings were that increased board diversity was found to significantly improve the readability of financial statements. Discretionary earnings management was found to significantly reduce the readability of financial statements. Sustainability reporting was found to significantly increase the readability of financial statements, and it moderated the relationship between board diversity, earnings management and financial statements readability in Kenya. Research limitations/implications: The study sample of 36 non-financial listed in the Nairobi Securities Exchange was very small and was affected by the problem of thin trading; hence, caution should be adopted when interpreting the findings. Practical implications: The Capital Markets Authorities (CMA) as a policymaker should enforce sustainability reporting by Kenyan listed firms as there is evidence that the reporting enhances the readability of financial statements. The Institute of Certified Public Accountants as a policymaker should closely monitor the published financial statements of firms for earnings management and punish the perpetrators, as there is empirical evidence that the practice reduces the readability of financial statements. Social implications: Sustainability reporting is successful as a moderating variable between readability of financial statements and determinants of readability of financial statements. Originality/value: This study contributes to knowledge by studying sustainability reporting as a moderating variable between the independent variables of board diversity and earnings management and the dependent variable of readability of financial statements and measured sustainability reporting using a dummy variable for the period before and after the enactment and release of CMA code of 2016 on corporate governance that required sustainability reporting by Kenyan listed companies. [ABSTRACT FROM AUTHOR]

Copyright of Çalışma ve Toplum is the property of Calisma ve Toplum and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper is an attempt to assess the impact of climate on wheat yield in the Indo-Gangetic Plain (IGP) region of India by using panel data analysis. Five IGP states namely Punjab, Haryana, Uttar Pradesh, Bihar, and West Bengal have been considered to frame a panel. The study used the data of climatic and non-climatic variables from 1990 to 2022 to achieve the objective of the study. The Im-Pesaran-Shin unit-root test was applied to check the stationarity of data. The results of the panel least square dummy variable model indicated that all the climatic variables had non significant influence. Among non-climatic variables that help increase wheat yield, fertilizer consumption and mechanization in agriculture were found to have a significant positive impact on wheat yield in the IGP region of India. [ABSTRACT FROM AUTHOR]

This study offers further evidence of the holiday effect on excess stock returns and volatility, and additional insights into its impact on the amusement park and attractions industry in the United States. The generalized auto-regressive conditional heteroscedasticity (GARCH) model and dummy variables are adopted to investigate pre- and post-holiday excess returns and volatility. Empirical results suggest the following: (1) both excess returns and excess volatility happen more frequently in pre-holidays than post-holidays; (2) the frequency of excess volatility is higher than excess returns for the holiday effect; and (3) nearly all significant excess volatilities are negative. We, therefore, conclude that the holiday effect exists in publicly traded amusement and theme parks. [ABSTRACT FROM AUTHOR]

This study examines the effects of sanctions implemented by the European Union and the United States of America against the Russian Federation following the latter's annexation of the Crimea Peninsula and invasion of Eastern Ukraine. The analysis focuses on indirect or spillover effects caused by EU and US sanctions on several macroeconomic variables of the Russian economy. Employing a structural vector autoregressive model, the study uses time-varying sanction indices to simulate sanctions, substituting for binary dummy variables traditionally employed in modelling sanction's implementation. The findings reveal that sanctions have an indirect impact on Russia's GDP through their direct effects on inflation, interest rate, and domestic currency – a notion introduced as "sanction transmission mechanism". [ABSTRACT FROM AUTHOR]

In this study, we propose a cointegration test in which structural changes are modeled gradually through the Fourier approach and the nonlinear structure is modeled with an exponential smooth transition autoregressive (ESTAR) model. In the traditional tests, structural changes are mostly modeled by dummy variables. However, the extended literature supposes that the flexible Fourier approach is an alternative approach to dummy variables since there is no need to know the form, date, and number of breaks with the Fourier approach. The lack of a cointegration test to combine the ESTAR model and Fourier approach in the literature constitutes the motivation of this study. Monte Carlo simulation results demonstrate that the size properties are close to the nominal size and the power of the test shows good performance even in the small sample simulation experiment. Results also indicate that the proposed test shows better power performance compared to the ESTAR cointegration test ignoring breaks. [ABSTRACT FROM AUTHOR]

The research aimed to study the current status of Egypt's most important exports of fruit crops in order to determine the most important variables that attract these crops in global markets by using the gravity model. The results of the study showed through the estimated model for the orange crop that there is a significant direct relationship for each of the population of the importing countries, the average foreign per capita share of the gross domestic product, the ratio between the Egyptian gross domestic product and the foreign gross domestic product, and the language variable on the quantity of Egyptian exports, while there was a significant inverse relationship for each of the geographical distance and the time factor on the quantity of Egyptian exports. The results of the gravity model for the grape crop also showed a significant direct relationship for each of the foreign gross domestic product and the language variable on the quantity of Egyptian exports, while there was no significant effect of the inverse relationship of the geographical distance on the quantity of Egyptian exports. The results of the gravity model for strawberry crop showed that there is a significant direct relationship between the average foreign per capita share of GDP and the ratio between the average Egyptian per capita share of GDP to the average foreign per capita share of GDP on the quantity of Egyptian exports, while there was a significant inverse relationship for geographical distance on the quantity of Egyptian exports, while the effect of the population in foreign countries was not proven significant, which the study may attribute to the clearly low population growth rates in these countries. [ABSTRACT FROM AUTHOR]

This paper investigates the evolution of the Ukrainian stock market through an analysis of various data properties, including persistence, volatility, normality, and resistance to anomalies for the case of daily returns from the PFTS stock index spanning 1995–2022. Segmented into sub-periods, it aims to test the hypothesis that the market's efficiency has increased over time. To do this different statistical techniques and methods are used, including R/S analysis, ANOVA analysis, regression analysis with dummy variables, t-tests, and others. The findings present a mixed picture: while volatility and persistence demonstrate a general decreasing trend, indicating a potential shift towards a more efficient market, normality tests reveal no discernible differences between analyzed periods. Similarly, the analysis of anomalies shows no specific trends in the market's resilience to the day-of-the-week effect. Overall, the results suggest a lack of systematic changes in data properties in the Ukrainian stock market over time, possibly due to the country's volatile conditions, including two revolutions, economic crises, the annexation of territories, and a Russian invasion leading to the largest war in Europe since WWII. The limited impact of reforms and changes justifies the need for continued market reform and evolution post-war. [ABSTRACT FROM AUTHOR]

Given the significant oil price changes during the past two decades, this paper examines the effect of oil price uncertainty on the sovereign credit risk of four Gulf Cooperation Council (GCC) countries (United Arab Emirates, Qatar, Bahrain, and Saudi Arabia). By utilizing the nonparametric local linear dummy variable estimates (LLDVE), we unveil a significant effect of oil uncertainty on the credit risk mainly during the oil price crash of 2014–2015 and to a lesser extent during the early period of the COVID-19 pandemic. This effect, however, is heterogeneous across the GCC countries. Unlike previous studies, our study controls for structural shifts, nonlinearities, and long-term trends in the relationship between oil price uncertainty and credit risk. Our findings hold important policy implications for policymakers and investors. [ABSTRACT FROM AUTHOR]

This paper examines the impact of entrepreneurs' inventor identity and entrepreneur patent participation on enterprise performance using the panel data of 358 firms from the Science and Technology Innovation Board in China from 2014–2019. The methodology applied in this study is ordinary least square (OLS) regression. Inventor identity (a dummy variable) and patent participation (measured by Entrepreneur Degree Centrality and Entrepreneur Betweenness Centrality) are independent variables. The dependent variable enterprise performance is split into patent quantity, speed, and quality. The results show that an entrepreneur's inventor identity positively affects the patent quantity and quality but slows the patent speed. Entrepreneur Degree Centrality negatively affects the patent quantity and quality but increases the patent speed. Entrepreneur Betweenness Centrality positively affects the patent quantity and quality, and there is a U-shape association between Entrepreneur Betweenness Centrality and patent speed. The findings suggest that entrepreneurs who are inventors can actively improve the patent quantity and quality. Still, an entrepreneur's full participation in patent may negatively affect the patent quantity and quality. Entrepreneurs should participate in a patent in a direction-oriented or node-controlled way. [ABSTRACT FROM AUTHOR]

Air pollution is the leading environmental hazard that has devastating health effects on human health. Identification of trends and patterns of air pollution is crucial in a specific geographic area to inform and implement effective control and mitigation measurements. Standard air pollution monitoring networks are costly for most low-and middle-income countries. The aim of this study was to investigate the variations in the mean concentrations of air pollutants that occur at various times of the day, days of the week, months of the year, and seasons during the period of January 2019 to December 2019 in Kandy, Sri Lanka using low-cost sensors. A regression analysis with dummy variables was used to model the relationship between the concentrations of air pollutants and categorical independent variables, the time of day and day of the week. Considering temporal variations, the study found that pollution is highest in the early morning and evening, and lowest in the afternoon. The worst days for the air quality were Wednesdays and Thursdays, while Sundays had the lowest PM2.5 pollution. The analysis of air quality variations by month showed that December had the lowest air pollution of the examined months. The variation in PM2.5 was greatly influenced by wind speed, wind direction, and rainfall patterns. The study emphasized the importance of long-term consistently management and monitoring of air pollution levels. [ABSTRACT FROM AUTHOR]

The Bayesian vector autoregressive (BVAR) model with the Minnesota prior proposed by Litterman [Litterman RB. Forecasting with bayesian vector autoregressions-five years of experience. J Business Economic Statist.1986;4(1):25–38.] has been very successful in multivariate time series modelling, providing better forecasting performance. However, the conventional Minnesota prior for BVAR depends only on the latest lag; in turn, it is not suitable for multivariate long memory time series forecasting. This study extends BVAR to (possibly) high-dimensional long memory time series. To this end, we incorporate a vector heterogeneous autoregressive (VHAR) structure to accommodate long-term persistence and impose priors on distant lags as well. Our proposed Bayesian VHAR (BVHAR) models, the so-called BVHAR-S and BVHAR-L, are easy to implement by using the Normal-inverse-Wishart prior and added dummy variable approach of [Bańbura M, Giannone D, Reichlin L. Large bayesian vector auto regressions. J Appl Econom. 2010;25(1):71–92.]. A supporting simulation study also proves posterior consistency. We further apply our models to nine CBOE Volatility Indices (VIXs) and show that our BVHAR models perform best in forecasting with the narrowest forecasting region. [ABSTRACT FROM AUTHOR]

Background: This investigation elucidates the significance of radiation emergency medicine (REM) within South Korea, while addressing the multifaceted challenges linked to the education of medical personnel in the field of radiation emergency responses. The efficacy of REM training initiatives has undergone scrupulous evaluation through a variety of techniques, including, but not limited to, the application of the DISASTER Paradigm and engagement in simulation-based training exercises. Materials and Methods: The present research is structured to evaluate the incremental utility of REM training programs by applying the Difference-in-Difference (DID) estimation using OLS regression methodologies. Simultaneously, it aims to suggest potential improvements to existing training modules. Central to the methodology is the estimation of the DID model via the 'sm.ols' function in the Python programming environment. In the equation 'outcome ~ T_d + P_t + T_d * P_t', 'outcome' denotes the dependent variable under review, 'T_d' signifies the treatment dummy variable, and 'P_t' represents the period dummy variable. The interaction term 'T_d * P_t' elucidates the average effect of the treatment post-intervention, taking into account the temporal trend. Results: The conclusions drawn from this scholarly investigation have manifested negative net utilities across the three pivotal DISASTER Paradigm indicators (T, E, and R). Through the adept implementation of a Python-infused computational methodology, this study has yielded results characterized by precision and veracity. These insights furnish empirical evidence, indicating that the intervention in question may not have yielded an enhancement in the net utility for the designated target cohort. Conclusion: This scholarly inquiry underscores the efficacy and meticulous precision of OLS-DiD estimations executed via a Python-centric computational approach. The empirical findings emanating from this research serve to fortify a robust foundation for the strategic navigation of unique challenges within the intersecting realms of nuclear science and medical studies, with particular emphasis on advancing the field of radiation emergency medicine (REM) education. [ABSTRACT FROM AUTHOR]

This article describes a method of curve-fitting that involves splitting the curve into two (or more) and creating separate functions for each part. These functions are then concatenated via a linking function to create one overall function that better describes the original data than is otherwise achievable. The linking function is able to do this by separating the original two functions so that they are each active in only the relevant portion of the overall curve without the use of dummy variables. The final result is a continuous function in which it is straightforward to smooth the transition at the knot between the piecewise subfunctions. In addition, the piecewise subfunctions do not need to align at the knot since the degree of smoothing is readily controlled. All types of functions may be concatenated so that the method is flexible and relatively simple to apply. [ABSTRACT FROM AUTHOR]

This study empirically examines the motives of Indian firms' managers to violate Generally Accepted Accounting Principles (GAAP) and engage in so-called fraudulent accounting. Managers' motivations for fraudulent accounting rather than profit adjustment are empirically analyzed using data from Indian-listed companies. The sample includes 91 firms and the period of study is March 2001 to March 2022. The tests were conducted using single and multiple variables by the empirical methods used in other studies on profit adjustment. In the case of single variables, the tests are the chi-square test of independence for dummy variables and the significance test of the difference between the mean and median for continuous variables. In the case of multiple variables, the sample firms with a dependent variable of 1 and the control firms with a dependent variable of 0 are analyzed using the logit model. The estimation is done by the robust covariance method. The findings indicate that firms that engage in fraudulent accounting are significantly worse off than other firms in terms of their financial position and operating results, have significantly higher financing needs, and significantly more frequently conduct initial public offerings (IPOs). [ABSTRACT FROM AUTHOR]

This study investigates the effects of the US unilateral trade policy on exports from beneficiary developing countries (BDCs) to the US market. It utilizes the Gravity model framework of international trade, employing the core gravity equation introduced by Tinbergen (1962) and Poyhonen (1963), and augments it with Batra's (2006) framework to account for other determinants of trade. The analysis focuses on factors such as Generalized Scheme of Preferences (GSP) imports, logistic performance index (LPI), Human Development Index (HDI), Concentration index, Global Innovation Index (GII), and dummy variables representing the African Growth & Opportunity Act (AGOA), Least Developing Countries (LDC), and landlocked countries. Surprisingly, the study finds that GDP has a negative impact, while distance has a positive impact on exports, contrary to the assumptions of the gravity model. The results also show a positive effect of GSP imports, but a significant negative impact of the AGOA variable on BDC exports. Moreover, the study highlights the positive influences of LPI and HDI on BDC exports. However, the Global Innovation Index and Concentration Index do not show significant effects on BDC exports. Therefore, the study suggests that BDCs should focus on enhancing human development and infrastructure to exploit export potential, as highlighted by the study's findings. [ABSTRACT FROM AUTHOR]

In pandemic conditions, Indonesia's macroeconomic situation experienced very sharp changes. Inflation and BI 7 Day Repo Rate (BI7DRR) set record lows. Gross Domestic Product (GDP) has also experienced a decline, which last occurred during the economic crisis in 1998. This research was conducted to analyze the influence of inflation, BI7DRR and GDP on the Net Asset Value (NAV) of conventional and sharia mutual funds as well as the influence of mutual fund types on mutual fund NAV. Panel data regression was carried out on mutual fund NAV, inflation, BI7DRR and GDP data collected quarterly during the 2013-2022 period. Dummy variables are used in regression analysis to determine whether there is a significant difference between the NAV of islamic and conventional mutual funds. The estimation results using the random effect model show that inflation and BI7DRR are not proven to have a significant effect on the NAV of sharia and conventional mutual funds, while GDP is proven to have a positive and significant effect on the NAV of sharia and conventional mutual funds. It is also not proven that there is a significant difference in the NAV of sharia and conventional mutual funds, so it is stated that the type of mutual fund has no effect on the NAV of mutual funds. [ABSTRACT FROM AUTHOR]

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The crucial role of financial systems in sustainable development has become the center of global and national interest and development strategies, and more developed and stable financial systems are the cornerstone for development finance and achieving the sustainable development goals. This research aims to examine the impact of financial development and stability on human development firstly in a sample of 185 countries worldwide, and secondly in a sample of 21 MENA countries, over the period 1990–2019. This study adopts the Panel Data Econometrics (Fixed Effects and Random Effects Methods) and proxy for human development by exploiting the UNDP's Human Development Index and its three subindices (Education, Income, and Life expectancy), while it adopts broad money, credit to the domestic private sector, and banking sector deposits (all as a percentage of GDP) to proxy for financial development. Moreover, it represents financial stability by the banking sector Z-score and a dummy variable representing crises. The empirical results reveal the prevailing role of development and stability in the financial systems on human development and its three subcomponents, regardless of the country's economic development level. [ABSTRACT FROM AUTHOR]

Malaysia's commitment to ecological quality is evident in its agendas for achieving a sustainable environment by 2030. However, hindrances like challenges towards energy transition efforts, stable political institutions, unclean economic globalization practices, and transiting to a complex economy could potentially delay the nation's attainment of its environmental goals. Against this backdrop, the current study focuses on examining the roles of political risk, economic globalization, energy transition, and economic complexity on ecological sustainability in Malaysia. In contrast to previous studies, this study innovatively incorporates a dummy variable to account for potential structural breaks that may occur during the study period. Furthermore, this study used an innovative environmental proxy which incorporates both the demand and supply aspect of the environment during computation. Using the time series data from 1984 to 2018 and employed the ARDL estimator method, the empirical results indicate that economic globalization is associated with ecological deterioration in Malaysia in the long run. On the other hand, economic complexity, political stability, and energy transition are shown to promote ecological sustainability in Malaysia in the long run. Moreover, the bootstrap time varying causality method demonstrates that each of the regressors has the capacity to predict the load capacity factor during distinct sub-periods. [ABSTRACT FROM AUTHOR]

The dummy variable based general linear model (gLM) is commonly used to model categorical factors and their interactions. However, the main factors and their interactions in a general linear model are often correlated even when the factors are independently distributed. Alternatively, the classical two-way factorial analysis of variance (ANOVA) model can avoid the correlation between the main factors and their interactions when the main factors are independent. But the ANOVA model is hardly applicable to a regular linear regression model especially in the presence of other covariates due to constraints on its model parameters. In this study, a centered general linear model (cgLM) is proposed for modeling interactions between categorical factors based on their centered dummy variables. We show that the cgLM can avoid the correlation between the main factors and their interactions as the ANOVA model when the main factors are independent. Meanwhile, similar to gLM, it can be used in regular regression and fitted conveniently using the standard least square approach by choosing appropriate baselines to avoid constraints on its model parameters. The potential advantage of cgLM over gLM for detection of interactions in model building procedures is also illustrated and compared via a simulation study. Finally, the cgLM is applied to a postmortem brain gene expression data set. [ABSTRACT FROM AUTHOR]

This study examined the effects of public infrastructural development and industrialization on economic complexity in Africa, and how industrialization is moderating the effect of public infrastructural development on economic complexity. The study adopted the pooled ordinary least squares, the dynamic system generalized method of moments and the dynamic bias‐corrected least squares dummy variable estimators with a panel of 34 African economies over the period 2010–2021. The findings revealed that public infrastructural development and industrialization contribute positively and significantly toward enhancing economic complexity in Africa. We also found that industrialization substantially moderates and enhances the influence of public infrastructural development on economic complexity. Furthermore, the results show that trade openness, foreign direct investment inflow, international tourism arrival and institutional quality are potent factors enhancing Africa's economic complexity, while the role of human capital remains muted. These findings suggest that African leaders and policy‐makers can foster economic complexity across the continent by embracing policies that prioritize increased infrastructural development, industrialization, trade openness and foreign direct investment inflow while improving the quality of institutions within the region. [ABSTRACT FROM AUTHOR]

This study aimed to obtain empirical proof of tax uncertainty and tax avoidance's impact on e-commerce businesses. Regression data panels with independent variable variations in tax rates and tax types were used as dummy variables. This study found that this type of tax affects tax avoidance, whereas variations in tax rates show no effect. In ecommerce businesses, various types of taxes are avoided. The types of taxes vary between countries that affect tax avoidance. The calculation mechanism can have an impact on changing selling prices which can reduce e-commerce margins. Margin is the determinant of CETR as a tax avoidance formulation, which can be used as a consideration for e-commerce to make its tax avoidance strategy. Tax avoidance can be viewed as opportunistic action committed by taxpayers. E-commerce businesses can also avoid taxes by considering the types of taxes they receive. This research examines tax uncertainty about e-commerce tax legislation. Tax uncertainty occurs because of variations in tax rates and the type of tax imposed on e-commerce transactions in each country, which contain risks for crossborder e-commerce players. It has never been analyzed in terms of tax avoidance efforts. [ABSTRACT FROM AUTHOR]

Unemployment is a factor that heavily influences the output of each economy. It is, therefore, one of the main concerns of any government worldwide. This study identifies key determinants of unemployment. By constructing an econometric model for the registered unemployment rate in Slovakia, the period from 2013 to 2022 was under scrutiny, while the impact of the COVID crisis was considered in the model through a dummy variable. Potential determinants of unemployment were selected based on theoretical knowledge and other scientific works, that is, average interest rates, gross minimum wage, GDP, inflation, exports, imports, government spending, corruption index, COVID-19 crisis, and month of the year. The final relevant factors for unemployment were tested and validated: interest rates, GDP, inflation, government spending, and exports. These study results may be valuable for the government when designing targeted interventions to optimise the unemployment rate in Slovakia or similar economies by influencing other macroeconomic indicators. [ABSTRACT FROM AUTHOR]

Structural breaks represent periods of turmoil that may influence how tourism affects economic growth. Current research on the tourism-led growth hypothesis (TLGH) measures the effect of structural breaks using dummy variables in regression models. However, the drawback of this approach is that there could be multiple structural breaks which result in an overfitting problem and reduce degrees of freedom in small samples. It also becomes difficult to isolate the effect of individual breaks when multiple structural breaks occur within the same year. We thus highlight the role of the Fourier ARDL model in addressing these shortcomings. We use three Pacific Island Countries: Fiji, Tonga, and Vanuatu as case studies to evaluate the efficacy of the Fourier ARDL model. Contrary to earlier research, our results indicate that tourism does not always lead to economic growth. Appropriate modelling of structural breaks also influences the outcome of asymmetric effects. These findings imply that future research should pay close attention to the effects of structural breaks in the TLGH. [ABSTRACT FROM AUTHOR]